Training Datasets   
Our experiments focus on learning from variable TSP20-50 graphs.
We also compare to training on fixed graph sizes TSP20, TSP50, TSP100, which have been the default choice in TSP literature.
In the supervised learning paradigm, we generate a training set of 1,280,000 TSP samples and groundtruth tours using Concorde.
Models are trained using the Adam optimizer [1]} for 10 epochs with a batch size of 128 and a fixed learning rate \(1e-4\) .
For reinforcement learning, models are trained for 100 epochs on 128,000 TSP samples which are randomly generated for each epoch (without optimal solutions) with the same batch size and learning rate.
Thus, both learning paradigms see 12,800,000 TSP samples in total.
